Spaces:
Running
Running
major update
#46
by
MINGYISU
- opened
- Scores/VLM2Vec-Qwen2-VL-V2.0-scores_report.json +1870 -0
- Scores/VLM2Vec-Qwen2-VL-V2.1-scores_report.json +1702 -0
- app.py +98 -7
- utils.py +48 -19
- utils_v2.py +149 -0
Scores/VLM2Vec-Qwen2-VL-V2.0-scores_report.json
ADDED
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@@ -0,0 +1,1870 @@
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|
Scores/VLM2Vec-Qwen2-VL-V2.1-scores_report.json
ADDED
|
@@ -0,0 +1,1702 @@
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|
| 1 |
+
{
|
| 2 |
+
"metadata": {
|
| 3 |
+
"model_name": "VLM2Vec-Qwen2-VL-V2.1",
|
| 4 |
+
"model_size": 2,
|
| 5 |
+
"embedding_dimension": null,
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| 6 |
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"max_length_tokens": null,
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| 7 |
+
"model_release_date": "2025-05-15",
|
| 8 |
+
"data_source": "",
|
| 9 |
+
"url": "",
|
| 10 |
+
"report_generated_date": "2025-05-30T06:33:26.437452"
|
| 11 |
+
},
|
| 12 |
+
"metrics": {
|
| 13 |
+
"image": {
|
| 14 |
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"ImageNet-1K": {
|
| 15 |
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"hit@1": 0.786,
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| 16 |
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| 17 |
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| 18 |
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| 19 |
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| 20 |
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| 21 |
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| 22 |
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| 23 |
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| 24 |
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| 25 |
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| 26 |
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| 27 |
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| 28 |
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| 29 |
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| 30 |
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| 31 |
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| 32 |
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| 33 |
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| 34 |
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| 35 |
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| 36 |
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| 37 |
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| 38 |
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},
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| 39 |
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| 40 |
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| 41 |
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| 42 |
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| 43 |
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| 44 |
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| 48 |
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| 49 |
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| 51 |
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| 52 |
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| 53 |
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| 54 |
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| 56 |
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| 57 |
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| 58 |
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| 59 |
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| 60 |
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| 61 |
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| 62 |
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| 63 |
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},
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| 64 |
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| 65 |
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| 66 |
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| 73 |
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| 74 |
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| 75 |
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| 76 |
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| 77 |
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| 78 |
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| 79 |
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| 84 |
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| 86 |
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| 87 |
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| 88 |
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},
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| 89 |
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| 90 |
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| 1702 |
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|
app.py
CHANGED
|
@@ -1,4 +1,5 @@
|
|
| 1 |
from utils import *
|
|
|
|
| 2 |
|
| 3 |
global data_component
|
| 4 |
|
|
@@ -10,12 +11,20 @@ def update_table(query, min_size, max_size, selected_tasks=None):
|
|
| 10 |
filtered_df = filtered_df[selected_columns]
|
| 11 |
return filtered_df
|
| 12 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
| 13 |
with gr.Blocks() as block:
|
| 14 |
gr.Markdown(LEADERBOARD_INTRODUCTION)
|
| 15 |
|
| 16 |
with gr.Tabs(elem_classes="tab-buttons") as tabs:
|
| 17 |
# Table 1
|
| 18 |
-
with gr.TabItem("📊 MMEB", elem_id="qa-tab-table1", id=1):
|
| 19 |
with gr.Row():
|
| 20 |
with gr.Accordion("Citation", open=False):
|
| 21 |
citation_button = gr.Textbox(
|
|
@@ -54,7 +63,7 @@ with gr.Blocks() as block:
|
|
| 54 |
|
| 55 |
with gr.Row():
|
| 56 |
tasks_select = gr.CheckboxGroup(
|
| 57 |
-
choices=TASKS_V1
|
| 58 |
value=TASKS_V1,
|
| 59 |
label="Select tasks to Display",
|
| 60 |
elem_id="tasks-select"
|
|
@@ -97,13 +106,95 @@ with gr.Blocks() as block:
|
|
| 97 |
)
|
| 98 |
refresh_button.click(fn=refresh_data, outputs=data_component)
|
| 99 |
|
| 100 |
-
|
| 101 |
-
|
| 102 |
-
|
| 103 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 104 |
|
| 105 |
# table 3
|
| 106 |
-
with gr.TabItem("
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 107 |
with gr.Row():
|
| 108 |
gr.Markdown(SUBMIT_INTRODUCTION, elem_classes="markdown-text")
|
| 109 |
|
|
|
|
| 1 |
from utils import *
|
| 2 |
+
import utils_v2 as v2
|
| 3 |
|
| 4 |
global data_component
|
| 5 |
|
|
|
|
| 11 |
filtered_df = filtered_df[selected_columns]
|
| 12 |
return filtered_df
|
| 13 |
|
| 14 |
+
def update_table_v2(query, min_size, max_size, selected_tasks=None):
|
| 15 |
+
df = v2.get_df()
|
| 16 |
+
filtered_df = v2.search_and_filter_models(df, query, min_size, max_size)
|
| 17 |
+
if selected_tasks and len(selected_tasks) > 0:
|
| 18 |
+
selected_columns = v2.BASE_COLS + selected_tasks
|
| 19 |
+
filtered_df = filtered_df[selected_columns]
|
| 20 |
+
return filtered_df
|
| 21 |
+
|
| 22 |
with gr.Blocks() as block:
|
| 23 |
gr.Markdown(LEADERBOARD_INTRODUCTION)
|
| 24 |
|
| 25 |
with gr.Tabs(elem_classes="tab-buttons") as tabs:
|
| 26 |
# Table 1
|
| 27 |
+
with gr.TabItem("📊 MMEB-V1", elem_id="qa-tab-table1", id=1):
|
| 28 |
with gr.Row():
|
| 29 |
with gr.Accordion("Citation", open=False):
|
| 30 |
citation_button = gr.Textbox(
|
|
|
|
| 63 |
|
| 64 |
with gr.Row():
|
| 65 |
tasks_select = gr.CheckboxGroup(
|
| 66 |
+
choices=TASKS_V1,
|
| 67 |
value=TASKS_V1,
|
| 68 |
label="Select tasks to Display",
|
| 69 |
elem_id="tasks-select"
|
|
|
|
| 106 |
)
|
| 107 |
refresh_button.click(fn=refresh_data, outputs=data_component)
|
| 108 |
|
| 109 |
+
with gr.TabItem("📊 MMEB-V2", elem_id="qa-tab-table1", id=2):
|
| 110 |
+
with gr.Row():
|
| 111 |
+
with gr.Accordion("Citation", open=False):
|
| 112 |
+
citation_button2 = gr.Textbox(
|
| 113 |
+
value=v2.CITATION_BUTTON_TEXT,
|
| 114 |
+
label=CITATION_BUTTON_LABEL,
|
| 115 |
+
elem_id="citation-button",
|
| 116 |
+
lines=10,
|
| 117 |
+
)
|
| 118 |
+
gr.Markdown(v2.TABLE_INTRODUCTION)
|
| 119 |
+
|
| 120 |
+
with gr.Row():
|
| 121 |
+
search_bar2 = gr.Textbox(
|
| 122 |
+
placeholder="Search models...",
|
| 123 |
+
show_label=False,
|
| 124 |
+
elem_id="search-bar"
|
| 125 |
+
)
|
| 126 |
+
|
| 127 |
+
df2 = v2.get_df()
|
| 128 |
+
min_size2, max_size2 = get_size_range(df2)
|
| 129 |
+
|
| 130 |
+
with gr.Row():
|
| 131 |
+
min_size_slider2 = gr.Slider(
|
| 132 |
+
minimum=min_size2,
|
| 133 |
+
maximum=max_size2,
|
| 134 |
+
value=min_size2,
|
| 135 |
+
step=0.1,
|
| 136 |
+
label="Minimum number of parameters (B)",
|
| 137 |
+
)
|
| 138 |
+
max_size_slider2 = gr.Slider(
|
| 139 |
+
minimum=min_size2,
|
| 140 |
+
maximum=max_size2,
|
| 141 |
+
value=max_size2,
|
| 142 |
+
step=0.1,
|
| 143 |
+
label="Maximum number of parameters (B)",
|
| 144 |
+
)
|
| 145 |
+
|
| 146 |
+
with gr.Row():
|
| 147 |
+
tasks_select2 = gr.CheckboxGroup(
|
| 148 |
+
choices=v2.TASKS,
|
| 149 |
+
value=v2.TASKS,
|
| 150 |
+
label="Select tasks to Display",
|
| 151 |
+
elem_id="tasks-select"
|
| 152 |
+
)
|
| 153 |
+
|
| 154 |
+
data_component2 = gr.components.Dataframe(
|
| 155 |
+
value=df2[v2.COLUMN_NAMES],
|
| 156 |
+
headers=v2.COLUMN_NAMES,
|
| 157 |
+
type="pandas",
|
| 158 |
+
datatype=v2.DATA_TITLE_TYPE,
|
| 159 |
+
interactive=False,
|
| 160 |
+
visible=True,
|
| 161 |
+
max_height=2400,
|
| 162 |
+
)
|
| 163 |
+
|
| 164 |
+
refresh_button2 = gr.Button("Refresh")
|
| 165 |
+
|
| 166 |
+
def update_with_tasks_v2(*args):
|
| 167 |
+
return update_table_v2(*args)
|
| 168 |
+
|
| 169 |
+
search_bar2.change(
|
| 170 |
+
fn=update_with_tasks_v2,
|
| 171 |
+
inputs=[search_bar2, min_size_slider2, max_size_slider2, tasks_select2],
|
| 172 |
+
outputs=data_component2
|
| 173 |
+
)
|
| 174 |
+
min_size_slider2.change(
|
| 175 |
+
fn=update_with_tasks_v2,
|
| 176 |
+
inputs=[search_bar2, min_size_slider2, max_size_slider2, tasks_select2],
|
| 177 |
+
outputs=data_component2
|
| 178 |
+
)
|
| 179 |
+
max_size_slider2.change(
|
| 180 |
+
fn=update_with_tasks_v2,
|
| 181 |
+
inputs=[search_bar2, min_size_slider2, max_size_slider2, tasks_select2],
|
| 182 |
+
outputs=data_component2
|
| 183 |
+
)
|
| 184 |
+
tasks_select2.change(
|
| 185 |
+
fn=update_with_tasks_v2,
|
| 186 |
+
inputs=[search_bar2, min_size_slider2, max_size_slider2, tasks_select2],
|
| 187 |
+
outputs=data_component2
|
| 188 |
+
)
|
| 189 |
+
refresh_button.click(fn=v2.refresh_data, outputs=data_component)
|
| 190 |
|
| 191 |
# table 3
|
| 192 |
+
with gr.TabItem("📝 About", elem_id="qa-tab-table2", id=3):
|
| 193 |
+
gr.Markdown(LEADERBOARD_INFO, elem_classes="markdown-text")
|
| 194 |
+
# gr.Image("overview.png", width=900, label="Dataset Overview")
|
| 195 |
+
|
| 196 |
+
# table 4
|
| 197 |
+
with gr.TabItem("🚀 Submit here! ", elem_id="submit-tab", id=4):
|
| 198 |
with gr.Row():
|
| 199 |
gr.Markdown(SUBMIT_INTRODUCTION, elem_classes="markdown-text")
|
| 200 |
|
utils.py
CHANGED
|
@@ -12,14 +12,13 @@ HF_TOKEN = os.environ.get("HF_TOKEN")
|
|
| 12 |
|
| 13 |
BASE_COLS = ["Rank", "Models", "Model Size(B)", "Data Source"]
|
| 14 |
TASKS_V1 = ["V1-Overall", "I-CLS", "I-QA", "I-RET", "I-VG"]
|
| 15 |
-
|
| 16 |
-
COLUMN_NAMES = BASE_COLS + TASKS_V1 + TASKS_V2
|
| 17 |
|
| 18 |
DATA_TITLE_TYPE = ['number', 'markdown', 'str', 'markdown'] + \
|
| 19 |
-
['number'] * len(TASKS_V1
|
| 20 |
|
| 21 |
LEADERBOARD_INTRODUCTION = """
|
| 22 |
-
# 📊 **MMEB LEADERBOARD
|
| 23 |
|
| 24 |
## Introduction
|
| 25 |
We introduce a novel benchmark, **MMEB-V1 (Massive Multimodal Embedding Benchmark)**,
|
|
@@ -59,7 +58,7 @@ SUBMIT_INTRODUCTION = """# Submit on MMEB Leaderboard Introduction
|
|
| 59 |
|
| 60 |
## ⚠ Please note that you need to submit the JSON file with the following format:
|
| 61 |
|
| 62 |
-
### **TO SUBMIT V1 ONLY**
|
| 63 |
```json
|
| 64 |
[
|
| 65 |
{
|
|
@@ -76,26 +75,56 @@ SUBMIT_INTRODUCTION = """# Submit on MMEB Leaderboard Introduction
|
|
| 76 |
]
|
| 77 |
```
|
| 78 |
|
| 79 |
-
###
|
|
|
|
|
|
|
| 80 |
```json
|
| 81 |
-
|
| 82 |
-
{
|
| 83 |
"Model": "<Model Name>",
|
| 84 |
"URL": "<Model URL>" or null,
|
| 85 |
"Model Size(B)": 1000 or null,
|
| 86 |
"Data Source": "Self-Reported",
|
| 87 |
-
"
|
| 88 |
-
"
|
| 89 |
-
|
| 90 |
-
|
| 91 |
-
"
|
| 92 |
-
|
| 93 |
-
|
| 94 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 95 |
```
|
| 96 |
-
You are also welcome to submit both versions by including all the fields above! :) \n
|
| 97 |
You may refer to the [**GitHub page**](https://github.com/TIGER-AI-Lab/VLM2Vec) for instructions about evaluating your model. \n
|
| 98 |
-
Please send us an email at [email protected], attaching the JSON file. We will review your submission and update the leaderboard accordingly.
|
|
|
|
| 99 |
"""
|
| 100 |
|
| 101 |
def create_hyperlinked_names(df):
|
|
@@ -124,7 +153,7 @@ def create_hyperlinked_names(df):
|
|
| 124 |
def get_df(file="results.jsonl"):
|
| 125 |
df = pd.read_json(file, orient='records', lines=True)
|
| 126 |
df['Model Size(B)'] = df['Model Size(B)'].apply(process_model_size)
|
| 127 |
-
for task in TASKS_V1
|
| 128 |
if df[task].isnull().any():
|
| 129 |
df[task] = df[task].apply(lambda score: '-' if pd.isna(score) else score)
|
| 130 |
df = df.sort_values(by=['V1-Overall'], ascending=False)
|
|
|
|
| 12 |
|
| 13 |
BASE_COLS = ["Rank", "Models", "Model Size(B)", "Data Source"]
|
| 14 |
TASKS_V1 = ["V1-Overall", "I-CLS", "I-QA", "I-RET", "I-VG"]
|
| 15 |
+
COLUMN_NAMES = BASE_COLS + TASKS_V1
|
|
|
|
| 16 |
|
| 17 |
DATA_TITLE_TYPE = ['number', 'markdown', 'str', 'markdown'] + \
|
| 18 |
+
['number'] * len(TASKS_V1)
|
| 19 |
|
| 20 |
LEADERBOARD_INTRODUCTION = """
|
| 21 |
+
# 📊 **MMEB LEADERBOARD**
|
| 22 |
|
| 23 |
## Introduction
|
| 24 |
We introduce a novel benchmark, **MMEB-V1 (Massive Multimodal Embedding Benchmark)**,
|
|
|
|
| 58 |
|
| 59 |
## ⚠ Please note that you need to submit the JSON file with the following format:
|
| 60 |
|
| 61 |
+
### **TO SUBMIT V1 ONLY (Depreciating)**
|
| 62 |
```json
|
| 63 |
[
|
| 64 |
{
|
|
|
|
| 75 |
]
|
| 76 |
```
|
| 77 |
|
| 78 |
+
### ***Important Notes: We will be releasing MMEB-V2 soon!***
|
| 79 |
+
### ***In V2, the detailed scores of each dataset will be included, and our code will automatically generate the results and calculate the overall scores.***
|
| 80 |
+
### **A V2 Submission would look like this: (TO BE RELEASED SOON)**
|
| 81 |
```json
|
| 82 |
+
{
|
| 83 |
+
"metadata": {
|
| 84 |
"Model": "<Model Name>",
|
| 85 |
"URL": "<Model URL>" or null,
|
| 86 |
"Model Size(B)": 1000 or null,
|
| 87 |
"Data Source": "Self-Reported",
|
| 88 |
+
"V1-Overall": 50.0,
|
| 89 |
+
"V2-Overall": 50.0
|
| 90 |
+
},
|
| 91 |
+
"metrics": {
|
| 92 |
+
"image": {
|
| 93 |
+
"ImageNet-1K": {
|
| 94 |
+
"hit@1": 0.5,
|
| 95 |
+
"ndcg@1": 0.5,
|
| 96 |
+
... ...
|
| 97 |
+
},
|
| 98 |
+
"N24News": {
|
| 99 |
+
... ...
|
| 100 |
+
},
|
| 101 |
+
... ...
|
| 102 |
+
},
|
| 103 |
+
"visdoc": {
|
| 104 |
+
"ViDoRe": {
|
| 105 |
+
"hit@1": 0.5,
|
| 106 |
+
"ndcg@1": 0.5,
|
| 107 |
+
... ...
|
| 108 |
+
},
|
| 109 |
+
... ...
|
| 110 |
+
},
|
| 111 |
+
"video": {
|
| 112 |
+
"DiDeMo": {
|
| 113 |
+
"hit@1": 0.5,
|
| 114 |
+
"ndcg@1": 0.5,
|
| 115 |
+
... ...
|
| 116 |
+
},
|
| 117 |
+
"MSR-VTT": {
|
| 118 |
+
... ...
|
| 119 |
+
},
|
| 120 |
+
... ...
|
| 121 |
+
}
|
| 122 |
+
}
|
| 123 |
+
}
|
| 124 |
```
|
|
|
|
| 125 |
You may refer to the [**GitHub page**](https://github.com/TIGER-AI-Lab/VLM2Vec) for instructions about evaluating your model. \n
|
| 126 |
+
Please send us an email at [email protected], attaching the JSON file. We will review your submission and update the leaderboard accordingly. \n
|
| 127 |
+
Please also share any feedback or suggestions you have for improving the leaderboard experience. We appreciate your contributions to the MMEB community!
|
| 128 |
"""
|
| 129 |
|
| 130 |
def create_hyperlinked_names(df):
|
|
|
|
| 153 |
def get_df(file="results.jsonl"):
|
| 154 |
df = pd.read_json(file, orient='records', lines=True)
|
| 155 |
df['Model Size(B)'] = df['Model Size(B)'].apply(process_model_size)
|
| 156 |
+
for task in TASKS_V1:
|
| 157 |
if df[task].isnull().any():
|
| 158 |
df[task] = df[task].apply(lambda score: '-' if pd.isna(score) else score)
|
| 159 |
df = df.sort_values(by=['V1-Overall'], ascending=False)
|
utils_v2.py
ADDED
|
@@ -0,0 +1,149 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
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|
|
|
|
|
|
|
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|
|
|
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|
| 1 |
+
import json
|
| 2 |
+
import os
|
| 3 |
+
import pandas as pd
|
| 4 |
+
from utils import create_hyperlinked_names
|
| 5 |
+
|
| 6 |
+
def sum_lst(lst):
|
| 7 |
+
assert isinstance(lst, list) and lst, f"Input should be a non-empty list, got {type(lst)}, size {len(lst)}"
|
| 8 |
+
total = lst[0]
|
| 9 |
+
for item in lst[1:]:
|
| 10 |
+
assert isinstance(item, (list, int, float)), f"Expected types are list and numbers, got {type(item)}"
|
| 11 |
+
total += item
|
| 12 |
+
return total
|
| 13 |
+
|
| 14 |
+
SCORE_BASE_DIR = "Scores"
|
| 15 |
+
META_DATA = ["model_name", "model_size", "url"]
|
| 16 |
+
DATASETS = {
|
| 17 |
+
"image": {
|
| 18 |
+
"I-CLS": ['VOC2007', 'N24News', 'SUN397', 'ObjectNet', 'Country211', 'Place365', 'ImageNet-1K', 'HatefulMemes', 'ImageNet-A', 'ImageNet-R'],
|
| 19 |
+
"I-QA": ['OK-VQA', 'A-OKVQA', 'DocVQA', 'InfographicsVQA', 'ChartQA', 'Visual7W-Pointing', 'ScienceQA', 'GQA', 'TextVQA', 'VizWiz'],
|
| 20 |
+
"I-RET": ['VisDial', 'CIRR', 'VisualNews_t2i', 'VisualNews_i2t', 'MSCOCO_t2i', 'MSCOCO_i2t', 'NIGHTS', 'WebQA', 'FashionIQ', 'Wiki-SS-NQ', 'OVEN', 'EDIS'],
|
| 21 |
+
"I-VG": ['MSCOCO', 'RefCOCO', 'RefCOCO-Matching', 'Visual7W']
|
| 22 |
+
},
|
| 23 |
+
"visdoc": {
|
| 24 |
+
"VisDoc": ['ViDoRe_arxivqa', 'ViDoRe_docvqa', 'ViDoRe_infovqa', 'ViDoRe_tabfquad', 'ViDoRe_tatdqa', 'ViDoRe_shiftproject', 'ViDoRe_syntheticDocQA_artificial_intelligence', 'ViDoRe_syntheticDocQA_energy', 'ViDoRe_syntheticDocQA_government_reports', 'ViDoRe_syntheticDocQA_healthcare_industry', 'VisRAG_ArxivQA', 'VisRAG_ChartQA', 'VisRAG_MP-DocVQA', 'VisRAG_SlideVQA', 'VisRAG_InfoVQA', 'VisRAG_PlotQA', 'ViDoSeek-page', 'ViDoSeek-doc', 'MMLongBench-page', 'MMLongBench-doc']
|
| 25 |
+
},
|
| 26 |
+
"video": {
|
| 27 |
+
"V-CLS": ['K700', 'UCF101', 'HMDB51', 'SmthSmthV2', 'Breakfast'],
|
| 28 |
+
"V-QA": ['Video-MME', 'MVBench', 'NExTQA', 'EgoSchema'],
|
| 29 |
+
"V-RET": ['MSR-VTT', 'MSVD', 'DiDeMo', 'VATEX', 'YouCook2'],
|
| 30 |
+
"V-MRET": ['QVHighlight', 'Charades-STA', 'MomentSeeker', 'ActivityNetQA']
|
| 31 |
+
}
|
| 32 |
+
}
|
| 33 |
+
ALL_DATASETS_SPLITS = {k: sum_lst(list(v.values())) for k, v in DATASETS.items()}
|
| 34 |
+
ALL_DATASETS = sum_lst(list(ALL_DATASETS_SPLITS.values()))
|
| 35 |
+
MODALITIES = list(DATASETS.keys())
|
| 36 |
+
SPECIAL_METRICS = {
|
| 37 |
+
'__default__': 'hit@1',
|
| 38 |
+
}
|
| 39 |
+
|
| 40 |
+
BASE_COLS = ['Rank', 'Models', 'Model Size(B)']
|
| 41 |
+
TASKS = ["Overall", "Image-Overall", "I-CLS", "I-QA", "I-RET", "I-VG", "VisDoc", "Video-Overall", "V-CLS", "V-QA", "V-RET", "V-MRET"]
|
| 42 |
+
COLUMN_NAMES = BASE_COLS + TASKS
|
| 43 |
+
|
| 44 |
+
DATA_TITLE_TYPE = ['number', 'markdown', 'str', 'markdown'] + \
|
| 45 |
+
['number'] * len(TASKS)
|
| 46 |
+
|
| 47 |
+
TABLE_INTRODUCTION = """"""
|
| 48 |
+
|
| 49 |
+
LEADERBOARD_INFO = """
|
| 50 |
+
## Dataset Summary
|
| 51 |
+
"""
|
| 52 |
+
|
| 53 |
+
CITATION_BUTTON_TEXT = r""""""
|
| 54 |
+
|
| 55 |
+
def load_single_json(file_path):
|
| 56 |
+
with open(file_path, 'r') as file:
|
| 57 |
+
data = json.load(file)
|
| 58 |
+
return data
|
| 59 |
+
|
| 60 |
+
def load_data(base_dir=SCORE_BASE_DIR):
|
| 61 |
+
all_data = []
|
| 62 |
+
for file_name in os.listdir(base_dir):
|
| 63 |
+
if file_name.endswith('-scores_report.json'):
|
| 64 |
+
file_path = os.path.join(base_dir, file_name)
|
| 65 |
+
data = load_single_json(file_path)
|
| 66 |
+
all_data.append(data)
|
| 67 |
+
return all_data
|
| 68 |
+
|
| 69 |
+
def calculate_score(raw_scores=None):
|
| 70 |
+
"""This function calculates the overall average scores for all datasets as well as avg scores for each modality and sub-task based on the raw scores.
|
| 71 |
+
Algorithm summary:
|
| 72 |
+
"""
|
| 73 |
+
def get_avg(sum_score, leng):
|
| 74 |
+
avg = sum_score / leng if leng > 0 else 0.0
|
| 75 |
+
avg = round(avg, 2) # Round to 2 decimal places
|
| 76 |
+
return avg
|
| 77 |
+
|
| 78 |
+
avg_scores = {}
|
| 79 |
+
overall_scores_summary = {} # Stores the scores sum and length for each modality and all datasets
|
| 80 |
+
for modality, datasets_list in DATASETS.items(): # Ex.: ('image', {'I-CLS': [...], 'I-QA': [...]})
|
| 81 |
+
overall_scores_summary[modality] = (0.0, 0) # Initialize the sum and count for each modality
|
| 82 |
+
for sub_task, datasets in datasets_list.items(): # Ex.: ('I-CLS', ['VOC2007', 'N24News', ...])
|
| 83 |
+
sub_task_sum_score, sub_task_datasets_len = 0.0, len(datasets)
|
| 84 |
+
for dataset in datasets: # Ex.: 'VOC2007'
|
| 85 |
+
score = raw_scores.get(modality, {}).get(dataset, 0.0)
|
| 86 |
+
score = 0.0 if score == "FILE_N/A" else score
|
| 87 |
+
metric = SPECIAL_METRICS.get(dataset, 'hit@1')
|
| 88 |
+
if isinstance(score, dict):
|
| 89 |
+
score = score.get(metric, 0.0)
|
| 90 |
+
sub_task_sum_score += score
|
| 91 |
+
|
| 92 |
+
sub_task_overall = get_avg(sub_task_sum_score, sub_task_datasets_len)
|
| 93 |
+
avg_scores[sub_task] = sub_task_overall
|
| 94 |
+
|
| 95 |
+
# Accumulate the scores sum and length for the each modality
|
| 96 |
+
modality_sum_score, modality_datasets_len = overall_scores_summary[modality]
|
| 97 |
+
modality_sum_score += sub_task_sum_score
|
| 98 |
+
modality_datasets_len += sub_task_datasets_len
|
| 99 |
+
overall_scores_summary[modality] = (modality_sum_score, modality_datasets_len)
|
| 100 |
+
|
| 101 |
+
all_datasets_sum_score, all_datasets_len = 0.0, 0
|
| 102 |
+
for modality, (modality_sum_score, modality_datasets_len) in overall_scores_summary.items():
|
| 103 |
+
name = f"{modality.capitalize()}-Overall"
|
| 104 |
+
avg_scores[name] = get_avg(modality_sum_score, modality_datasets_len)
|
| 105 |
+
# Accumulate the scores sum and length for all datasets
|
| 106 |
+
all_datasets_sum_score += modality_sum_score
|
| 107 |
+
all_datasets_len += modality_datasets_len
|
| 108 |
+
avg_scores['Overall'] = get_avg(all_datasets_sum_score, all_datasets_len)
|
| 109 |
+
return avg_scores
|
| 110 |
+
|
| 111 |
+
def generate_model_row(data):
|
| 112 |
+
metadata = data['metadata']
|
| 113 |
+
row = {
|
| 114 |
+
'Models': metadata.get('model_name', None),
|
| 115 |
+
'Model Size(B)': metadata.get('model_size', None),
|
| 116 |
+
'URL': metadata.get('url', None)
|
| 117 |
+
}
|
| 118 |
+
scores = calculate_score(data['metrics'])
|
| 119 |
+
row.update(scores)
|
| 120 |
+
return row
|
| 121 |
+
|
| 122 |
+
def get_df():
|
| 123 |
+
"""Generates a DataFrame from the loaded data."""
|
| 124 |
+
all_data = load_data()
|
| 125 |
+
rows = [generate_model_row(data) for data in all_data]
|
| 126 |
+
df = pd.DataFrame(rows)
|
| 127 |
+
df = df.sort_values(by='Overall', ascending=False).reset_index(drop=True)
|
| 128 |
+
df['Rank'] = range(1, len(df) + 1)
|
| 129 |
+
df = create_hyperlinked_names(df)
|
| 130 |
+
|
| 131 |
+
return df
|
| 132 |
+
|
| 133 |
+
def refresh_data():
|
| 134 |
+
df = get_df()
|
| 135 |
+
return df[COLUMN_NAMES]
|
| 136 |
+
|
| 137 |
+
def search_and_filter_models(df, query, min_size, max_size):
|
| 138 |
+
filtered_df = df.copy()
|
| 139 |
+
|
| 140 |
+
if query:
|
| 141 |
+
filtered_df = filtered_df[filtered_df['Models'].str.contains(query, case=False, na=False)]
|
| 142 |
+
|
| 143 |
+
size_mask = filtered_df['Model Size(B)'].apply(lambda x:
|
| 144 |
+
(min_size <= 1000.0 <= max_size) if x == 'unknown'
|
| 145 |
+
else (min_size <= x <= max_size))
|
| 146 |
+
|
| 147 |
+
filtered_df = filtered_df[size_mask]
|
| 148 |
+
|
| 149 |
+
return filtered_df[COLUMN_NAMES]
|